NSF 25-509: Emerging Mathematics in Biology (eMB)

Research Focus

The Emerging Mathematics in Biology (eMB) program funds integrative research that applies novel or foundational mathematical approaches to address significant biological questions. The program targets development of innovative mathematical theories, techniques, and computational methods—including AI/deep learning/machine learning (AI/DL/ML) foundations enabling explainable AI or mechanistic insight—to model and analyze complex, dynamic, heterogeneous biological systems across all scales (molecular, cellular, organismal, population, ecosystem, evolutionary). Priority research areas include mathematical foundations of AI/DL/ML in biomathematics; methods for limited or noisy data; probabilistic-deterministic model integration; genomics and omics applications; organismal development, physiology, morphology, biomechanics, behavior, and neuroscience; dynamical organism interactions (host-symbiont, predator-prey, disease dynamics); organism-environment interactions and ecological-evolutionary feedbacks; phylogenetic frameworks; automated extraction and annotation of large biological datasets; biotechnology applications; climate-biodiversity modeling; and respiratory infection transmission dynamics. The program emphasizes balanced collaboration between mathematical and biological scientists to generate new biological insights and enhance predictive power.

At a Glance

• Who can apply: U.S. institutions of higher education (two- and four-year, including community colleges); no PI restrictions or proposal limits per organization or individual.
• Funding & project length: $2–6 million total for FY25–26 across 10–15 awards; individual projects up to 3 years.
• Award mechanism: Standard Grant or Continuing Grant.
• Key dates: Full proposal deadline March 2, 2026, then March 1 annually; no letters of intent or preliminary proposals required.
• Best fit for: Mathematical biologists, computational biologists, and interdisciplinary teams combining mathematical sciences (dynamical systems, statistics, AI/ML theory) with biological sciences (genomics, ecology, physiology, disease modeling).